Fitted Values by Variable in R-CodePudding

I want to see the fitted values by variable while still taking into account all other variables in the model. I would describe these as partial/marginal fitted values. I have a toy example below. This dataset is built into R. Is this possible?

Seatbelts <- data.frame(Seatbelts)
head(Seatbelts)

Seatbelts<-Seatbelts[complete.cases(Seatbelts),]

## 75% of the sample size
smp_size <- floor(0.75 * nrow(Seatbelts))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(Seatbelts)), size = smp_size)

train <- Seatbelts[train_ind, ]
test <- Seatbelts[-train_ind, ]

# glm()
m1 <- glm(DriversKilled  ~  front   rear   kms   PetrolPrice   VanKilled   law,
          family=poisson(link = "log"),
          data=train)
fitted(m1)

CodePudding user response：

Here you get the values of the variables with the fitted value of the glm model in a dataframe:

data.frame(front = train$front, rear = train$rear, kms = train$kms, 
           PetrolPrice = train$PetrolPrice, VanKilled = train$VanKilled, 
           law = train$law, Fitted = fitted(m1))

CodePudding user response：

I'm not sure, but I think you're looking for an effects plot (or the corresponding data).

library(effects)
plot(allEffects(m1))

To get the specific values, you could use (e.g.)as.data.frame(allEffects(m1)) (which returns a list of data frames, one for each predictor variable).

The emmeans package has similar/overlapping functionality.